Paired Bid-Based Double Auction Mechanism for RAN Slicing in 5G-and-Beyond System

Sun, S., Feng, X., Qin, S., Sun, Y. and Wang, G. (2020) Paired Bid-Based Double Auction Mechanism for RAN Slicing in 5G-and-Beyond System. In: 2020 IEEE 20th International Conference on Communication Technology, 28-31 Oct 2020, pp. 533-538. ISBN 9781728181417 (doi: 10.1109/ICCT50939.2020.9295796)

Abstract

Network slicing has been widely deemed as a promising technology that enables the sharing of infrastructure resources for 5G-and-beyond mobile networks. Infrastructure Providers (InPs) abstract physical network into multiple isolated network slices, each of which can be operated as a virtual network by different Mobile Virtual Network Operators (MVNOs). However, the asymmetric information between resource supply of InP and usage requirement of MVNO challenges the resource allocation when enforcing slicing in the radio access network (RAN). In this paper, we propose a paired bid-based double-auction mechanism for a slicing-based RAN to improve resource allocation efficiency. We construct a market model in which the MVNOs and the InPs submit respective bids and ask Network Slice Broker (NSB) for slice transactions, and the NSB determines the winner pairs and corresponding payments to clear the slice market by maximizing social welfare. Numerical results validate the effectiveness of our proposed mechanism on improving the overall network resource allocation efficiency without collecting full information on the competitive strategies and utility functions of the MVNOs and InPs.

• Item Type: Conference Proceedings
• Additional Information: This work was supported by the National Science Foundation of China under Grant number 62071091, the China Postdoctoral Science Foundation under Grant number 2019M663476.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao
• Authors: Sun, S., Feng, X., Qin, S., Sun, Y., and Wang, G.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2576-7828
• ISBN: 9781728181417
• Published Online: 24 December 2020
• Copyright Holders: Copyright © 2020 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher